Muutke küpsiste eelistusi

Practical Spreadsheet Modeling Using @Risk [Kõva köide]

(EpiX Analytics, Boulder, Colorado, USA), (Loras College, Dubuque, IA)
  • Formaat: Hardback, 222 pages, kõrgus x laius: 178x254 mm, kaal: 612 g, 200 Illustrations, black and white
  • Ilmumisaeg: 02-Dec-2019
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-10: 0367173867
  • ISBN-13: 9780367173869
Teised raamatud teemal:
Practical Spreadsheet Modeling Using @RiskSuurem pilt
  • Formaat: Hardback, 222 pages, kõrgus x laius: 178x254 mm, kaal: 612 g, 200 Illustrations, black and white
  • Ilmumisaeg: 02-Dec-2019
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-10: 0367173867
  • ISBN-13: 9780367173869
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780367173869.html
Märksõnad:
Practical Spreadsheet Modeling Using @Risk provides a guide of how to construct applied decision analysis models in spreadsheets. The focus is on the use of Monte Carlo simulation to provide quantitative assessment of uncertainties and key risk drivers. The book presents numerous examples based on real data and relevant practical decisions in a variety of settings, including health care, transportation, finance, natural resources, technology, manufacturing, retail, and sports and entertainment. All examples involve decision problems where uncertainties make simulation modeling useful to obtain decision insights and explore alternative choices. Good spreadsheet modeling practices are highlighted. The book is suitable for graduate students or advanced undergraduates in business, public policy, health care administration, or any field amenable to simulation modeling of decision problems. The book is also useful for applied practitioners seeking to build or enhance their spreadsheet modeling skills.

Features











Step-by-step examples of spreadsheet modeling and risk analysis in a variety of fields





Description of probabilistic methods, their theoretical foundations, and their practical application in a spreadsheet environment





Extensive example models and exercises based on real data and relevant decision problems





Comprehensive use of the @Risk software for simulation analysis, including a free one-year educational software license
Preface xi
Acknowledgments xiii
Authors xv
Introduction xvii
1 Conceptual Maps and Models
1(22)
1.1 Introductory Case MoviePass
1(1)
1.2 First Steps: Visualization
2(5)
1.3 Retirement Planning Example
7(5)
1.4 Good Practices with Spreadsheet Model Construction
12(1)
1.5 Errors in Spreadsheet Modeling
12(2)
1.6 Decision Analysis
14(1)
1.7 Conclusion: Best Practices
15(8)
Exercises
16(5)
Notes
21(2)
2 Basic Monte Carlo Simulation in Spreadsheets
23(26)
2.1 Introductory Case: Retirement Planning
23(1)
2.2 Risk and Uncertainty
23(2)
2.3 Scenario Manager
25(2)
2.4 Monte Carlo Simulation
27(1)
2.4.1 Generating Random Numbers
27(1)
2.4.2 Monte Carlo Simulation for MoviePass
28(1)
2.5 Monte Carlo Simulation Using @Risk
28(10)
2.6 Monte Carlo Simulation for Retirement Planning
38(4)
2.7 Discrete Event Simulation
42(7)
Exercises
44(2)
Notes
46(3)
3 Selecting Distributions
49(32)
3.1 First Introductory Case: Valuation of a Public Company Using Expert Opinion
49(1)
3.2 Modeling Expert Opinion in the Valuation Model
50(5)
3.3 Second Introductory Case: Value at Risk---Fitting Distributions to Data
55(1)
3.4 Distribution Fitting for Value at Risk---Parameter and Model Uncertainty
56(9)
3.4.1 Parameter Uncertainty (More Advanced, Optional)
59(6)
3.4.2 Model Uncertainty (Most Advanced, Optional)
65(1)
3.5 Third Introductory Case: Failure Distributions
65(2)
3.6 Commonly Used Discrete Distributions
67(4)
3.7 Commonly Used Continuous Distributions
71(2)
3.8 A Brief Decision Guide for Selecting Distributions
73(8)
Exercises
74(4)
Notes
78(3)
4 Modeling Relationships
81(40)
4.1 First Example: Drug Development
81(3)
4.2 Second Example: Collateralized Debt Obligations
84(4)
4.3 Multiple Correlations Example: Cockpit Failures
88(4)
4.4 Copulas Example: How Correlated Are Home Prices?
92(5)
4.5 Empirical Copulas
97(3)
4.6 Fifth Example: Advertising Effectiveness
100(1)
4.7 Regression Modeling
101(4)
4.8 Simulation within Regression Models
105(2)
4.9 Multiple Linear Regression Models
107(4)
4.10 The Envelope Method
111(2)
4.11 Summary
113(8)
Exercises
114(4)
Notes
118(3)
5 Time Series Models
121(22)
5.1 The Need for Time Series Analysis: A Tale of Two Series
121(4)
5.2 Introductory Case: Air Travel and September 11
125(2)
5.3 Analyzing the Air Traffic Data and 9/11
127(3)
5.4 Second Example: Stock Prices
130(2)
5.5 Types of Time Series Models
132(1)
5.6 Third Example: Soybean Prices
133(1)
5.7 Fourth Example: Home Prices and Multivariate Time Series
134(9)
Exercises
137(4)
Notes
141(2)
6 Additional Useful Techniques
143(34)
6.1 Advanced Sensitivity Analysis
143(2)
6.2 Stress Testing
145(2)
6.3 Non-Parametric Distributions
147(4)
6.4 Case: An Insurance Problem
151(1)
6.5 Frequency and Severity
152(8)
6.6 The Compound Distribution
160(1)
6.7 Uncertainty and Variability
161(2)
6.8 Bayesian Analysis
163(14)
Exercises
169(4)
Notes
173(4)
7 Optimization and Decision Making
177(32)
7.1 Introductory Case: Airline Seat Pricing
177(1)
7.2 A Simulation Model of the Airline Pricing Problem
177(2)
7.3 A Simulation Table to Explore Pricing Strategies
179(2)
7.4 A Stochastic Optimization Solution to the Airline Pricing Problem
181(6)
7.5 Optimization with Multiple Decision Variables
187(3)
7.6 Adding Constraints
190(1)
7.7 Efficient Frontier
191(5)
7.8 Stochastic Dominance
196(4)
7.9 Summary
200(9)
Exercises
201(5)
Notes
206(3)
Appendix: Risk Analysis in Projects 209(8)
Index 217
Dale E. Lehman, PhD, is Professor of Business Administration and Director of the EMBA in Business Analytics at Loras College. He has taught at numerous universities in North America, Europe, and Asia. He has also published extensively in the areas of microeconomics, with applications in the telecommunications, health care, and natural resource industries. He has authored three previous books in these areas.

Huybert Groenendaal, PhD, is Managing Director at EpiX Analytics. He has extensive experience in using risk modeling to support decision making in fields that include business development, financial valuation, and R&D portfolio evaluation within the pharmaceutical and medical device industries, as well as health and epidemiology, energy, manufacturing and private equity. He regularly teaches risk analysis training classes.